I want to briefly revisit Hansen’s 1998 Congressional forecast.  Yes, I and many others have churned over this ground many times, but I think I now have a better approach.   The typical approach has been to overlay some actual temperature data set on top of Hansen’s forecast (e.g. here).  The problem is that with revisions to all of these data sets, particularly the GISS reset in 1999, none of these data sets match what Hansen was using at the time.  So we often get into arguments on where the forecast and actuals should be centered, etc.

This might be a better approach.  First, let’s start with Hansen’s forecast chart (click to enlarge).

Folks have argued for years over which CO2 scenario best matches history.  I would argue it is somewhere between A and B, but you will see in a moment that it almost does not matter.    It turns out that both A and B have nearly the same regressed slope.

The approach I took this time was not to worry about matching exact starting points or reconciling difference anomaly base periods.  I merely took the slope of the A and B forecasts and compared it to the slope over the last 30 years of a couple of different temeprature databases (Hadley CRUT4 and the UAH v6 satellite data).

The only real issue is the start year.  The analysis is not very sensitive to the year, but I tried to find a logical start.  Hansen’s chart is frustrating because his forecasts never converge exactly, even 20 years in the past.  However, they are nearly identical in 1986, a logical base year if Hansen was giving the speech in 1988, so I started there.  I didn’t do anything fancy on the trend lines, just let Excel calculate the least squares regression.  This is what we get (as usual, click to enlarge).

I think that tells the tale  pretty clearly.   Versus the gold standard surface temperature measurement (vs. Hansen’s thumb-on-the-scale GISS) his forecast was 2x too high.  Versus the satellite measurements it was 3x too high.

The least squares regression approach probably under-estimates that A scenario growth rate, but that is OK, that just makes the conclusion more robust.

By the way, I owe someone a thanks for the digitized numbers behind Hansen’s chart but it has been so many years since I downloaded them I honestly forgot who they came from.

For a variety of reasons I have been limited in blogging, but here is a brief roundup of interesting stories related to the science of anthropogenic global warming.

• Even by the EPA’s own alarmist numbers, a reduction in man-made warming of 0.01C in the year 2100 would cost $78 billion per year.  This is over $7 trillion a year per degree of avoided warming, again using even the EPA’s overly high climate sensitivity numbers.   For scale, this is almost half the entire US GDP.   This is why the precautionary principle was always BS – it assumed that the cost of action was virtually free.  Sure it makes sense to avoid low-likelihood but high-cost future contingencies if the cost of doing so is low.  But half of GDP?
• As I have written a zillion times, most of the projected warming from CO2 is not from CO2 directly but from positive feedback effects hypothesized in the climate.  The largest of these is water vapor.  Water is (unlike CO2) a strong greenhouse gas and if small amounts of warming increase water vapor in the atmosphere, that would be a positive feedback effect that would amplify warming.   Most climate modellers assume relative humidity stays roughly flat as the world warms, meaning total water vapor content in the atmosphere will rise.  In fact, this does not appear to have been the case over the last 50 years, as relative humidity has fallen while temperatures have risen.  Further, in a peer-reviewed article, scientists suggest certain negative feedbacks that would tend to reduce atmospheric water vapor.
• A new paper reduces the no-feedback climate sensitivity to CO2 from about 1-1.2C/doubling (which I and most other folks have been using) to something like 0.41C.  This is the direct sensitivity to CO2 before feedbacks, if I understand the paper correctly. without any reference to feedbacks.  In that sense, the paper seems to be wrong in comparing this sensitivity to the IPCC numbers, which are including feedbacks.  A more correct comparison is of the 0.41C to a number about 1.2C, which is what I think the IPCC is using.   Never-the-less, if correct, halving this sensitivity number should halve the post-feedback number.

My hypothesis continues to be that the post feedback climate sensitivity to CO2 number, expressed as degrees C per doubling of atmospheric CO2 concentrations, is greater than zero and less than one.

• It is pretty much time to stick a fork in the hide-the-decline debate.  This is yet another occasion when folks (in this case Mann, Briffa, Jones) should have said “yep, we screwed up” years ago and moved on.  Here is the whole problem in 2 charts.  Steve McIntyre recently traced the hide-the-decline trick (which can be summarized as truncating/hiding/obfuscating data that undermined their hypothesis on key charts) back to an earlier era.

I have posted on this a zillion times over here, and most of you are up to speed on this, but I posted this for my Coyote Blog readers and thought it would be good to repost over here.

Take all the psuedo-quasi-scientific stuff you read in the media about global warming.  Of all that mess, it turns out there is really only one scientific question that really matters on the topic of man-made global warming: Feedback.

While the climate models are complex, and the actual climate even, err, complexer, we can shortcut the reaction of global temperatures to CO2 to a single figure called climate sensitivity.  How many degrees of warming should the world expect for each doubling of CO2 concentrations  (the relationship is logarithmic, so that is why sensitivity is based on doublings, rather than absolute increases — an increase of CO2 from 280 to 290 ppm should have a higher impact on temperatures than the increase from, say, 380 to 390 ppm).

The IPCC reached a climate sensitivity to CO2 of about 3C per doubling.  More popular (at least in the media) catastrophic forecasts range from 5C on up to about any number you can imagine, way past any range one might consider reasonable.

But here is the key fact — Most folks, including the IPCC, believe the warming sensitivity from CO2 alone (before feedbacks) is around 1C or a bit higher (arch-alarmist Michael Mann did the research the IPCC relied on for this figure).  All the rest of the sensitivity between this 1C and 3C or 5C or whatever the forecast is comes from feedbacks (e.g. hotter weather melts ice, which causes less sunlight to be reflected, which warms the world more).  Feedbacks, by the way can be negative as well, acting to reduce the warming effect.  In fact, most feedbacks in our physical world are negative, but alarmist climate scientists tend to assume very high positive feedbacks.

What this means is that 70-80% or more of the warming in catastrophic warming forecasts comes from feedback, not CO2 acting alone.   If it turns out that feedbacks are not wildly positive, or even are negative, then the climate sensitivity is 1C or less, and we likely will see little warming over the next century due to man.

This means that the only really important question in the manmade global warming debate is the sign and magnitude of feedbacks.  And how much of this have you seen in the media?  About zero?  Nearly 100% of what you see in the media is not only so much bullshit (like whether global warming is causing the cold weather this year) but it is also irrelevant.  Entirely tangential to the core question.  Its all so much magician handwaving trying to hide what is going on, or in this case not going on, with the other hand.

To this end, Dr. Roy Spencer has a nice update.  Parts are a bit dense, but the first half explains this feedback question in layman’s terms.  The second half shows some attempts to quantify feedback.  His message is basically that no one knows even the sign and much less the magnitude of feedback, but the empirical data we are starting to see (which has admitted flaws) points to negative rather than positive feedback, at least in the short term.  His analysis looks at the change in radiative heat transfer in and out of the earth as measured by satellites around transient peaks in ocean temperatures (oceans are the world’s temperature flywheel — most of the Earth’s surface heat content is in the oceans).

Read it all, but this is an interesting note:

In fact, NO ONE HAS YET FOUND A WAY WITH OBSERVATIONAL DATA TO TEST CLIMATE MODEL SENSITIVITY. This means we have no idea which of the climate models projections are more likely to come true.

This dirty little secret of the climate modeling community is seldom mentioned outside the community. Don’t tell anyone I told you.

This is why climate researchers talk about probable ranges of climate sensitivity. Whatever that means!…there is no statistical probability involved with one-of-a-kind events like global warming!

There is HUGE uncertainty on this issue. And I will continue to contend that this uncertainty is a DIRECT RESULT of researchers not distinguishing between cause and effect when analyzing data.

If you find this topic interesting, I recommend my video and/or powerpoint presentation to you.

One of the defenses often used by climate modelers against charges that climate is simple to complex to model accurately is that “they do it all the time in finance and economics.”  This comes today from Megan McArdle on economic forecasting:

I find this pretty underwhelming, since private forecasters also unanimously think they can make forecasts, a belief which turns out to be not very well supported.  More than one analysis of these sorts of forecasts has found them not much better than random chance, and especially prone to miss major structural changes in the economy.   Just because toggling a given variable in their model means that you produce a given outcome, does not mean you can assume that these results will be replicated in the real world.  The poor history of forecasting definitionally means that these models are missing a lot of information, and poorly understood feedback effects.

Sounds familiar, huh?  I echoed these sentiments in a comparison of economic and climate forecasting here.

From the New Scientist:

What’s special about this latest dip is that the sun is having trouble starting the next solar cycle. The sun began to calm down in late 2007, so no one expected many sunspots in 2008. But computer models predicted that when the spots did return, they would do so in force. Hathaway was reported as thinking the next solar cycle would be a “doozy”: more sunspots, more solar storms and more energy blasted into space. Others predicted that it would be the most active solar cycle on record. The trouble was, no one told the sun.

The first sign that the prediction was wrong came when 2008 turned out to be even calmer than expected. That year, the sun was spot-free 73 per cent of the time, an extreme dip even for a solar minimum. Only the minimum of 1913 was more pronounced, with 85 per cent of that year clear.

As 2009 arrived, solar physicists looked for some action. They didn’t get it. The sun continued to languish until mid-December, when the largest group of sunspots to emerge for several years appeared. Finally, a return to normal? Not really.

Even with the solar cycle finally under way again, the number of sunspots has so far been well below expectations. Something appears to have changed inside the sun, something the models did not predict. But what?

Of course, Anthony Watt has been pointing this out for over two years, even pointing to a discontinuity in the Geomagnetic Average Planetary Index as one sign.

The people who model the sun, and failed, are not bad people.  It is an excercise worth attempting.  It turns out we just don’t know enough about the sun to accurately model its behavior.  Models are only as good as our understanding of the natural processes.  Something to think about with climate models.

The article onj climate modelling is here, and is pretty good.  My bit is below, from web page 3:

But Warren Meyer, a mechanical and aerospace engineer by training who blogs at www.climate-skeptic.com, said that climate models are highly flawed. He said the scientists who build them don’t know enough about solar cycles, ocean temperatures and other things that can nudge the earth’s temperature up or down. He said that because models produce results that sound impressively exact, they can give off an air of infallibility.

But, Meyer said — if the model isn’t built correctly — its results can be both precise-sounding and wrong.

“The hubris that can be associated with a model is amazing, because suddenly you take this sketchy understanding of a process, and you embody it in a model,” and it appears more trustworthy, Meyer said. “It’s almost like money laundering.”

I actually like my term “knowlege laundering.”

Today will actually be fun, because it involves criticism of some of my writing around what I find to be the most interesting issue in climate, that of feedback effects.  I have said for a while that greenhouse gas theory is nearly irrelevant to the climate debate, because most scientists believe that the climate sensitivity to CO2 acting along without feedbacks is low enough (1.2C per doubling) to not really be catastrophic.   So the question whether man-made warming will be catastrophic depends on the assumption of strong net positive feedbacks in the climate system.  B Kalafut believes I have the wrong mental model for thinking about feedback in climate, and I want to review his post in depth.

Naming positive feedbacks is easy. In paleoclimate, consider the effect of albedo changes at the beginning of an ice age or the “lagging CO2” at the end. In the modern climate, consider water vapor as a greenhouse gas, or albedo changes as ice melts. In everyday experience, consider convection’s role in sustaining a fire. Consider the nucleation of raindrops or snowflakes or bubbles in a pot of boiling water. At the cellular level, consider the voltage-gated behavior of the sodium channels in a nerve axon or the “negative damping” of hair cells in the cochlea.

I am assuming he is refuting my statement that “it is hard to find systems dominated by strong net positive feedbacks that are stable over long periods of time.”  I certainly never said individual positive feedbacks don’t exist, and even mentioned some related to climate, such as ice albedo and increases in water vapor in air.  I am not sure we are getting anywhere here, but his next paragraph is more interesting.

On to the meat of Meyer’s argument: he seizes on one word (“feedback”) and runs madly, from metaphor to mental model. Metaphor: “like in an ideal amplifier”. Model: The climate experiences linear feedback as in an amplifier–see the math in his linked post or in the Lindzen slides from which he gets the idea. And then he makes the even worse leap, to claiming that climate models (GCMs) “use” something called “feedback fractions”. They do not–they take no such parameters as inputs but rather attempt to simulate the effects of the various feedback phenomena directly. This error alone renders Meyer’s take worthless–it’s as though he enquires about what sort of oats and hay one feeds a Ford Mustang. Feedback in climate are also nonlinear and time-dependent–consider why the water vapor feedback doesn’t continue until the oceans evaporate–so the ideal amplifier model cannot even be “forced” to apply.

First, I don’t remember ever claiming that climate models used a straight feedback-amplification method.  And I am absolutely positive I never said GCM’s use feedback fractions.    I would not expect them to.    This is a total straw man.  I am using a simple feedback amplification model as an abstraction to represent the net results of the models in a way layman might understand, and backing into an implied fraction f from published warming forecasts and comparing them to the 1.2C non-feedback number.  Much in the same way that scientists use the concept of climate sensitivity to shortcut a lot of messy detail and non-linearity.  I am, however, open to the possibility that mine is a poor mental model, so lets think about it.

Let’s start with an analogy.  There are very complicated electronic circuits in my stereo amplifier.  Nowadays, when people design those circuits, they have sophisticated modeling programs that can do a time-based simulation of voltage and current at every point in the circuit.  For a simulated input, the program will predict the output, and show it over time, even if it is messy and non-linear.  These models are in some ways like climate models, except that we understand electronic components better so our parametrization is more precise and reliable.    All that being said, it does not change the fact that a simple feedback-gain model for sections of the complex amplifier circuitry is still a useful mental model for the process at some level of abstraction, as long as one understands the shortcomings that come from any such simplification.

The author is essentially challenging the use of Gain = 1/ (1-f) to represent the operation of the feedbacks here.  So let’s think about if this is appropriate.  Let’s begin with thinking about a single feedback, ice albedo.   The theory is that there is some amount of warming from CO2, call it dT.  This dT will cause more ice to melt than otherwise would have  (or less ice to form in the winter).  The ice normally reflects more heat and sunlight back into space than open ocean or bare ground, so when it is reduced, the Earth gets a small incremental heat flux that will result in an increase in temperatures.  We will call this extra increase in temperature f*dT where f is most likely a positive number less than one.  So now our total increase, call it dT’ is dT+f*dT.   But this increase of f*dT will in turn cause some more ice to melt.  By the same logic as above, this increase will be f*f*dT.  And so on in an infinite series.  The solution to this series for a constant value of f is  dT’ = dT/(1-f) … thus the formula above.

So the underlying operation of the feedback is the same:  Input –> output –> output modifies input.   There are not somehow different flavors or types of feedback that operate in radically different ways but have the same name  (as in his Mustang joke).

The author claims the climate models are building up the affects of the processes like ice albedo from its pieces, ie rather than abstracting in to the gain formula, the models are adding up all the individual pieces, on a grid, over time.  I am sure that is true.   The question is not whether they use the simplified feedback formula, but whether it is a useful abstraction.  I see nothing from my description of the ice albedo process to say it is not.

What happens if there are time delays?  Well, as long as f is less than 1, the system will reach steady state at some point and this formula should apply.  What happens if the feedback is non-liner?  Well, in most natural systems, it is almost certainly non-linear.   In our ice albedo example, f is almost certainly different at different temperatures levels  (for example, a change from -30C to -31C has a lot less effect on ice albedo than a change from 0C to 1C.   The factor f is probably also dependent on the amount of ice remaining, since in the limit when all the ice is melted there should be no further effect.  But I would argue that when we pull back and look at the forest instead of the trees, a critical skill for modelers who too often get buried in their minutia while losing the ability to reality-check their results, that the 1/(1-f) is still an interesting if imperfect abstraction for the results, particularly since we are looking at tenths of a degree, and its hard for me to believe that it is wildly non-linear over that kind of range.  (By the way, it is not at all unusual for mainstream alarmist scientists to use this same feedback formula as a useful though imperfect abstraction, for example  in Gerard H. Roe and Marcia B. Baker, “Why Is Climate Sensitivity So Unpredictable?”, Science 318 (2007): 629–632 Not free but summarized here.)

To determine if it is a useful abstraction, I would ask the author what conclusions I draw that fall apart.  I really only made two points with the use of feedback anyway.

1. I used the discussion to educate people that feedback is the main source of catastrophic warming, so that it should be the main focus of the scientific replication.   We can argue all day about time delays and non-linearity, but if the IPCC says the warming from CO2 alone is going to be 1.2C per doubling and the warming with all feedbacks considered is going to be, say, 4.8C per doubling (the author says himself that the models all converge at constant CO2), then we can say feedback is amplifying the initial man-made input by 4, or alternatively, 75% of the warming is from feedback effects, so these are probably where we need to focus.  I struggle to see how one can argue with this.
2. I used the simple gain formula to say if feedback were quadrupling temperatures, this implies a feedback factor of 0.75, and that this number is pretty dang high for a long-term stable system.  Yes, the feedback is non-linear, but I don’t think this is an unreasonable reality check on the models to see what sorts of average feedbacks are being produced by the parameters.

The author’s points on non-linearity and time delays are actually more relevant to the discussion in other presentations when I talked about whether the climate models that show high future sensitivities to CO2 are consistent with past history, particularly if warming in the surface temperature record is exaggerated by urban biases.  But even forgetting about these, it is really hard to reconcile sensitivities of, say, four degrees per doubling with history, where we have had about 0.6C (assuming irrationally that its all man-made) of warming in about 42% of a doubling  (the effect, I will add, is non-linear, so one should see more warming in the first half than the second half of a doubling).  Let’s leave out aerosols for today  (those are the great modeler’s miracle cure that allows every model, even those of widely varying CO2 sensitivities and feedback effects, all exactly back-cast to history).  These time delays and non-linearities could help reconcile the two, though my understanding is that the time delay is thought to be on the order of 12 years, which would not reconcile things at all.  I suppose one could assume non-linearity such that the feedback effects accelerate with time past some tipping point, but I will say I have yet to see any convincing physical study that points to this effect.

Well, the weather is lovely outside so I suppose I should get on with it:

Meyer draws heavily from a set of slides from a talk by Richard Lindzen before a noncritical audience. These slides are full of invective and conspiracy talk, and their scientific content is lousy. Specifically, Lindzen supposedly estimates effective linear feedbacks for various GCMs and finds some greater than one. The mathematics presented by Lindzen in his slides does not allow that, and he doesn’t provide details of how such things even could be inferred. An effective linear feedback greater than one implies a runaway process, yet GCMs are always run for finite time, so there cannot be divergence to infinity. Moreover, as far as I know, all of the GCMs are known to converge once CO2 is stabilized.

I draw on Lindzen and Lindzen is wrong about a bunch of stuff and Lindzen uses invective and conspiracy talk so, what?  Lindzen can answer all of this stuff.  I used one chart from Lindzen, and it wasn’t even about feedback  (I will reproduce it below).

I did mention that in theory, if the feedback factor is greater than one, in other words, if the first order feedback addition to input is greater than the original input, then the function rapidly runs away to infinity.  Which it does.  I don’t know what Lindzen has to say about this or what the author is referring to.   My only point is that when folks like Al Gore talk about runaway warming and Earth becoming Venus, they are really implying runaway positive feedback effects with feedback factors greater than one.  Since I really don’t go anywhere with this and in reality the author is debating Lindzen over an argument or analysis I am not even familiar with, I will leave this alone.  The only thing I will say is that his last sentence seems on point, but his second to last is double talk.  All he is saying is that by only solving a finite number of terms in a a divergent infinite series his calculations don’t go to infinity.  Duh.

I am open to considering whether I have the correct mental model.  But I reject the notion that it is wrong to try to simplify and abstract the operation of climate models.  I have not modeled the climate, but I have modeled complex financial, economic, and mechanical systems.  And here is what I can tell you from that experience — the more people tell me that they have modeled a system in the most minute parametrization, and that the models in turn are not therefore amenable to any abstraction, the less I trust their models.  These parameters are guesses, because there just isn’t enough understanding of the complex and chaotic climate system to parse out their different values, or to even be clear about cause and effect in certain processes  (like cloud formation).

I worry about the hubris of climate modelers, telling me that I am wrong and impossible to try to tease out one value for net feedback for the entire climate, and instead I should be thinking in terms of teasing out hundreds or thousands of parameters related to feedback.  This is what I call knowledge laundering:

These models, whether forecasting tools or global temperature models like Hansen’s, take poorly understood descriptors of a complex system in the front end and wash them through a computer model to create apparent certainty and precision.  In the financial world, people who fool themselves with their models are called bankrupt (or bailed out, I guess).  In the climate world, they are Oscar and Nobel Prize winners.

This has incorrectly been interpreted as my saying these folks are wrong for trying to model the systems.  Far from it — I have spend a lot of my life trying to model less complex systems.  I just want to see some humility.

Postscript: Here is the only chart that I know of in my presentation from Lindzen, and its not even in the video he links to, it is in this longer and more comprehensive video

That seems a reasonable enough challenge to me, particularly given the data in this post and this quote from Judith Currey, certainly not a skeptic:

They don’t disprove anthropogenic global warming, but we can’t airbrush them away. We need to incorporate them into the overall story. We had two bumps—in the ’90s and also in the ’30s and ’40s—that may have had the same cause. So we may have exaggerated the trend in the later half of the 20th century by not adequately interpreting these bumps from the ocean oscillations. I don’t have all the answers. I’m just saying that’s what it looks like.

Again, as I have said before, man’s CO2 is almost certainly contributing to a warming trend.  But when we really look at history objectively and tease out measurement problems and cyclical phenomena, we are going to find that this trend is entirely consistent with a zero to negative feedback assumption for the climate as a whole, meaning that man’s CO2 is driving 1.2C or less of warming per doubling of CO2 concentrations.

Given all the activity of late challenging various aspects of the IPCC’s work, I wanted to remind folks of probably the most important assumption in the IPCC (and related climate models) that seldom makes the media.

Greenhouse gas theory alone does not give us a catastrophe.  By the IPCC numbers, originally I think from Michael Mann in 1998, greenhouse warming from CO2 should be about 1.2C per doubling of CO2 concentrations.  But the IPCC gets a MUCH higher final number than this.  The reason is positive feedback.  This is a second theory, that the Earth’s temperature system is dominated by very strong net positive feedback effects.  Even if greenhouse gas theory is “settled,” it does not get us a catastrophe.  The catastrophe comes from the positive feedback theory, and this is most definitely not settled.

I usually put it this way to laymen:  Imagine the Earth’s climate is a car.  Greenhouse gas theory says CO2 will only give the car a nudge.  In most cases, this nudge will only move the car a little bit, because a lot of forces work to resist the nudge.  Climate theory, however, assumes that the car is actually perched precariously at the very top of a steep hill, such that a small nudge will actually start the car rolling downhill until in crashes.  This theory that the Earth is perched precariously on the top of the hill is positive feedback theory, and is far from settled.  In fact, a reasonable person can immediately challenge it by asking the sensible question  — “well, how has the climate managed to avoid a nudge (and resulting crash)  for hundreds of millions of years?”

I got to thinking about all this because I saw a chart of mine in Nicola Scafetta’s SPPI report on climate change, where he uses this chart:

I am happy he chose this chart, because it is one of my favorites.   It shows that most of the forecast warming from major alarmist models comes from the positive feedback theory, and not from greenhouse gas theory.  Let me explain how it is built.

The blue line at the bottom is based on an equation right out of the Third IPCC Report (the Fourth Report seems to assume it is still valid but does not include it anywhere I can find).  The equation seems to be from Mann 1998, and is for the warming effect from CO2 without feedbacks.   The equation is:

∆T = F(C2) – F(C1)
Where F(C) = Ln(1+1.2c+0.005c^2+0.0000014c^3)

So the blue line is just this equation where C1=385ppm and C2 is the concentration on the X axis.

The other lines don’t exist in the IPCC reports that I can find, though they should**.  What I did was to take various endpoint forecasts in the IPCC and from other sources and simply scale the blue line up, which implicitly assumes feedback acts uniformly across the range of concentrations.   So, for example, a forecast after feedback of 4.8C of warming around 800ppm was assumed to scale the blue no feedback line up by a uniform factor of 4.8/1.2 = 4x.  For those who know the feedback formula, we can back into the implied feedback fraction (again not to be found anywhere in the IPCC report) which would be  4=1/(1-f)  so f=75%, which is a quite high factor.

** This seems like a totally logical way to show the warming effect from CO2, but the IPCC always insists on showing just warming over time.  But this confuses the issue because it is also dependent on expected CO2 emissions forecasts.  I know there are issues of time delays, but I think a steady-state version of this chart would be helpful.

In most all of the climate models, the warming effect from feedback is actually much larger than the warming effect from CO2 alone.   That is why I have said for years that it is a waste of time to debate “greenhouse gas theory” as the real theory that matters to the proposition that climate sensitivity to CO2 is high is the theory that Earth’s temperature system is dominated by strong positive feedback.  And the largest feedback in climate models tends to be water vapor feedback, despite the fact that even the IPCC admits that such feedback is poorly understood.  To this end:

In a third paper, accepted for publication by the Journal of Theoretical and Applied Climatology, three scientists – two Australians and one American, revisit data on upper-atmospheric humidity. The three are Garth Paltridge, Albert Arking and Michael Pook, and they have found that, contrary to climate model predictions, water vapour in the upper atmosphere is acting as a brake on global warming.

Established climate models assume constant humidity at all levels in the atmosphere as the temperature rises. But, using data from weather balloons accumulated over 35 years, these researchers find this is not so. At the lower levels, it is higher than expected, dropping below normal at the higher altitudes.

This, they say, implies that “long-term water vapour feedback is negative – that it would reduce rather than amplify the response of the climate system to external forcing such as that from increasing atmospheric CO2.” This, in one fell swoop, challenges the central premise of the warmists that, once CO2 reaches a certain level, we experience runaway global warming.

When asked what one thing I would want to tell laymen about catastrophic man-made global warming theory, it is the following:  That this theory is in fact a two-part theory.  Greenhouse gas theory alone only gives us incremental warming and no catastrophe.  It is a second theory that Earth’s climate is dominated by strong positive feedbacks that multiplies warming of perhaps a degree over the next century from CO2 to 3,5, or more degrees of warming.  And while it is fairly well accepted by all that CO2 will cause a bit of warming alone, this second theory is not at all settled and in fact may even the the sign of the feedback wrong.

Two stories came out this week undercutting to of the largest sources of feedback.

1.  Water Vapor Feedback

Water vapor is a highly variable gas and has long been recognized as an important player in the cocktail of greenhouse gases—carbon dioxide, methane, halocarbons, nitrous oxide, and others—that affect climate.

“Current climate models do a remarkable job on water vapor near the surface. But this is different — it’s a thin wedge of the upper atmosphere that packs a wallop from one decade to the next in a way we didn’t expect,” says Susan Solomon, NOAA senior scientist and first author of the study.

Since 2000, water vapor in the stratosphere decreased by about 10 percent. The reason for the recent decline in water vapor is unknown. The new study used calculations and models to show that the cooling from this change caused surface temperatures to increase about 25 percent more slowly than they would have otherwise, due only to the increases in carbon dioxide and other greenhouse gases.

An increase in stratospheric water vapor in the 1990s likely had the opposite effect of increasing the rate of warming observed during that time by about 30 percent, the authors found.

2.  CO2  (outgassing from oceans) Feedback

The most alarming forecasts of natural systems amplifying the human-induced greenhouse effect may be too high, according to a new report.

The study in Nature confirms that as the planet warms, oceans and forests will absorb proportionally less CO2.

It says this will increase the effects of man-made warming – but much less than recent research has suggested….

The most likely value among their estimates suggests that for every degree Celsius of warming, natural ecosystems tend to release an extra 7.7 parts per million of CO2 to the atmosphere (the full range of their estimate was between 1.7 and 21.4 parts per million).

This stands in sharp contrast to the recent estimates of positive feedback models, which suggest a release of 40 parts per million per degree; the team say with 95% certainty that value is an overestimate.

OK readers, let’s see how close you have been paying attention.  The models have over-estimated this important feedback by a factor of 5 (40 to 7.7). As I have shown time and time again, the vast majority of the warming in climate forecasts is from feedback — about 1C per century is directly from CO2, the rest is from feedback multipliers.  Have a forecast that says 5C warming in the next century, then about 4C of that is probably due to feedback.

But remember this post, where I said

…there is a very strong social cost in academia to challenging global warming, so that even when findings in certain studies seem to undercut key pieces of the argument, the researches always add something like “but of course this does not refute the basic theory of global warming” at the end of the paper.

So what do this study’s author’s say?

The authors warn, though, that their research will not reduce projections of future temperature rises.

Further, they say their concern about man-made climate change remains high.

Of course, because if this factor goes down, they will just shore up their forecasts and keep them them high with some other plug variable.  Because no one is funding scientists (or quoting them in newspapers) whose models call for just 1C of warming over the next century.

In preparing for my climate presentation in Phoenix next week, I went back and read through Lindzen & Choi, a study whose results I linked here.  The study claims to have measured feedback, and have found feedback to temperature changes in the natural climate system to be negative –opposite of the assumption of strong positive feedback in climate models.  I found this interesting, as we often do of studies that confirm our own hypotheses.

Re-reading the study, I was uncomfortable with the methodology, but figured I was missing something.  Specifically, I didn’t understand how an increase in temperature could result in a decrease in outgoing radiation, as Lindzen says is assumed in all the models.   As I have always understood it, the opposite has to be true in a stable system.   With an added forcing, temperature increases which increases outgoing radiation until the radiation budget is back in balance.  Models that assumed otherwise would have near infinite temepratures.   I assumed perhaps that Lindzen & Choi were making measurements during the time the system came back into equilibrium.

Apparently, both Luboš Motl and Roy Spencer have spotted problems as well, and they explain the issue in a more sophisticated way here and here.

But the results I have been getting from the fully coupled ocean-atmosphere (CMIP) model runs that the IPCC depends upon for their global warming predictions do NOT show what Lindzen and Choi found in the AMIP model runs. While the authors found decreases in radiation loss with short-term temperature increases, I find that the CMIP models exhibit an INCREASE in radiative loss with short term warming.

In fact, a radiation increase MUST exist for the climate system to be stable, at least in the long term. Even though some of the CMIP models produce a lot of global warming, all of them are still stable in this regard, with net increases in lost radiation with warming (NOTE: If analyzing the transient CMIP runs where CO2 is increased over long periods of time, one must first remove that radiative forcing in order to see the increase in radiative loss).

So, while I tend to agree with the Lindzen and Choi position that the real climate system is much less sensitive than the IPCC climate models suggest, it is not clear to me that their results actually demonstrate this.

Spencer further makes the point he has made for a couple of years now that feedback is really, really, really hard to measure, because it is so easy to confuse cause and effect.

Spencer by the way points out this admission from the Fourth IPCC report:

“A number of diagnostic tests have been proposed…but few of them have been applied to a majority of the models currently in use. Moreover, it is not yet clear which tests are critical for constraining future projections (of warming). Consequently, a set of model metrics that might be used to narrow the range of plausible climate change feedbacks and climate sensitivity has yet to be developed.”

This is kind of amazing, in effect saying “we have no idea what the feedbacks are or how to measure them, but lacking any knowlege, we are going to consistently and universally assume very high positive feedbacks with feedback factors > 0.7”

Joe Romm has gone on the record at Climate Progress on April 13, 2009 that the “median” forecast was for warming in the US by 2100 of 10-15F, or 5.5-8.3C, and he made it very clear that if he had to pick a single number, it would be the high end of that range.

On average, the 8.3C implies about 0.9C per decade of warming.  This might vary slightly by what starting point he intended (he is not very clear in the post) and I understand there is a curve so it will be below average in the early years and above in the later.

Anyway, Joe Romm is ready to put his money where his mouth is, and wants to make a 50/50 bet with any comers that warming in the next decade will be… 0.15C.  Boy, it sure is daring for a guy who is constantly in the press at a number around 0.9C per decade to commit to a number 6 times lower when he puts his money where his mouth is.   Especially when Romm has argued that warming in the last decade has been suppressed (somehow) and will pop back up soon.  Lucia has more reasons why this is a chickensh*t bet.

I deconstructed a previous gutless bet by Nate Silver here.

I am looking at some back and forth in this Flowing Data post.

Apparently an Australian Legislator named Stephen Fielding posted this chart and asked, “Is it the case that CO2 increased by 5% since 1998 whilst global temperature cooled over the same period (see Fig. 1)?  If so, why did the temperature not increase; and how can human emissions be to blame for dangerous levels of warming?”

Certainly this could sustain some interesting debate.  Climate is complex, so their might be countervailing effects to CO2, but it also should be noted that none of the models really predicted this flatness in temperatures, so it certainly could be described as “unexpected” at least among the alarmist community.

Instead, the answer that came back from Stephen Few was this (as reported by Flowing Data, I cannot find this on Few’s site):

This is a case of someone who listens only to what he wants to hear (the arguments of a few fringe organizations with agendas) and either ignores or is incapable of understanding the overwhelming weight of scientific evidence. He selected a tiny piece of data (a short period of time, with only one of many measures of temperature), misinterpreted it, and ignored the vast collection of data that contradicts his position. This fellow is either incredibly stupid or a very bad man.

Every alarmist from Al Gore to James Hansen has used this same chart in their every presentation – showing global temperatures since 1950  (or really since 1980) going up in lockstep with Co2.  This is the alarmists #1 chart.  All Fielding has done is shown data after 1998, something alarmists tend to be reluctant to do.  Sure it’s a short time period, but nothing in any alarmist prediction or IPCC report hinted that there was any possibility that for even so short a time as 15 years warming might cease  (at least not in the last IPCC report, which I have read nearly every page of).  So, by using the alarmists’ own chart and questioning a temperature trend that went unpredicted, Fielding is “either incredibly stupid or a very bad man.”  Again, the alarmist modus operandi – it is much better to smear the person in ad hominem attacks than deal with his argument.

Shouldn’t there be symmetry here?  If it is OK for every alarmist on the planet to show 1980-1995 temperature growing in lockstep with CO2 as “proof” of a relationship, isn’t it equally OK to show 1995-2010 temperature not growing in lockstep with CO2 to question the relationship?  Why is one ok but the other incredibly stupid and/or mean-spirited?   I mean graphs like this were frequent five years ago, though they have dried up recently:

For extra credit, figure out how they got most of the early 2000’s to be warmer than 1998 in this chart, since I can find no major temperature metric that matches this.  I suspect some endpoint smoothing games here.

I won’t get into arguing the “overwhelming weight of scientific evidence” statement, as I find arguments over counting scientific heads or papers to be  useless in the extreme.  But I will say that as a boy when I learned about the scientific method, there was a key step where one’s understanding of a natural phenomenon is converted into predicted behaviors, and then those predictions are tested against reality.  All Fielding is doing is testing the predictions, and finding them to be missing the mark.  Sure, one can argue that the testing period has not been long enough, so we will keep testing, but what Fielding is trying to do here, however imperfectly, is perfectly compatible with the scientific method.

I must say I am a bit confused about those “many other measures of temperature.”  Is Mr. Few suggesting that the chart would have different results in Fahrenheit?  OK, I am kidding of course.  What I am sure he means is that there are groups other than the Hadley Center that produce temperature records for the globe  (though in Mr. Fielding’s defense the Hadley Center is a perfectly acceptable source and the preferred source of much of the IPCC report).  To my knowledge, there are four major metrics (Hadley, GISS, UAH, RSS).  Of these four, at least three (I am not sure about the GISS) would show the same results.  I think the “overwhelming weight” of temperature metrics makes the same point as Mr. Fielding’s chart.

In the rest of his language, Few is pretty sloppy for someone who wants to criticize someone for sloppiness.  He says that Fielding “misinterpreted” the temperature data.  How?  Seems straight forward to me.  He also says that there is a “vast collection of data that contradicts his position.”  What position is that?  If his position is merely that Co2 has increased for 15 years and temperatures have not, well, there really is NOT a vast collection of data that contradicts that.  There may be a lot of people who have published reasons whythis set of facts does not invalidate AGW, but the facts are still the same.

By the way, I get exhausted by the accusation that skeptics are somehow simplistic and can’t understand complex systems.    I feel like my understanding is pretty nuanced. By the way, its interesting how the sides have somewhat reversed here.  When temperature was going up steadily, it was alarmists saying that things were simple and skeptics saying that climate was complex and you couldn’t necessarily make the 1:1 correlation between CO2 and temperature increases.  Now that temperature has flat lined for a while, it is alarmists screaming that skeptics are underestimating the complexity.  I tend to agree — climate is indeed really really complex, though I think if one accepts this complexity it is hard to square with the whole “settled science” thing.  Really, we have settled the science in less than 20 years on perhaps the most complex system we have ever tried to understand?

The same Flowing Data post references this post from Graham Dawson.  Most of Dawson’s “answers” to Fieldings questions are similar to Few’s, but I wanted to touch on one or two other things.

First, I like how he calls findings from the recent climate synthesis report the “government answer” as if this makes it somehow beyond dispute.  But I digress.

The surface air temperature is just one component in the climate system (ocean, atmosphere, cryosphere). There has been no material trend in surface air temperature during the last 10 years when taken in isolation, but 13 of the 14 warmest years on record have occurred since 1995. Also global heat content of  the ocean (which constitutes 85% of the total warming) has continued to rise strongly in this period, and ongoing warming of the climate system as a whole is supported by a very wide range of observations, as reported in the peer-reviewed scientific literature.

This is the kind of blithe answer that is full of inaccuracies everyone needs to be careful about.  The first sentence is true, and the second is probably close to the mark, though with a bit more uncertainty than he implies.  He is also correct that global heat content of the ocean is a huge part of warming or the lack thereof, but his next statement is not entirely correct.  Ocean heat content as measured by the new ARGO system since 2003 has been flat to down.  Longer term measures are up, but most of the warming comes at the point the old metrics were spliced to the ARGO data, a real red flag to any serious data analyst.  The cryospehere is important as well, but most metrics show little change in total sea ice area, with losses in the NH offset by gains in the SH.

While the Earth’s temperature has been warmer in the geological past than it is today, the magnitude and rate of change is unusual in a geological context. Also the current warming is unusual as past changes have been triggered by natural forcings whereas there are no known natural climate forcings, such as changes in solar irradiance, that can explain the current observed warming of the climate system. It can only be explained by the increase in greenhouse gases due to human activities.

No one on Earth has any idea if the first sentence is true — this is pure supposition on the author’s part, stated as a fact.  We are talking about temperature changes today over a fifty year (or shorter) period, and we have absolutely no way to look at changes in the “geological past” on this fine of a timescale.  I am reminded of the old ice core chart that was supposedly the smoking gun between CO2 and temperature, only to find later as we improved the time resolution that temperature increases came before Co2 increases.

I won’t make too much of my usual argument on the sun, except to say that the Sun has been substantially more active during the warming period of 1950-2000 than it has been in other times.  What I want to point out, though, is the core foundation of the alarmist argument, one that I have pointed out before.  It boils down to:  Past warming must be due to man because we can’t think of what else it could be.   This is amazing hubris, representing a total unwillingness to admit what we do and don’t understand.  Its almost like the ancient Greeks, attributing what they didn’t understand in the cosmos to the hijinx of various gods.

It is not the case that all GCM computer models projected a steady increase in temperature for the period 1990-2008.  Air temperatures are affected by natural variability.  Global Climate Models show this variability in the long term but are not able to predict exactly when such variations will happen. GCMs can and do simulate decade-long periods of no warming, or even slight cooling, embedded in longer-term warming trends.

But none showed zero warming, or anything even close.

Vegas casinos love the sucker bet.  Nothing makes the accountants happier than seeing someone playing the Wheel of Fortune, or betting on “12, the hard way” in craps, or taking insurance in blackjack.  While the house always maintains a slim advantage, these bets really stack the deck in the house’s favor.

And just as I don’t feel guilty for leaving Caesar’s Palace without playing the Wheel of Fortune, I don’t feel a bit of guilt for not taking this bet from Nate Silver:

1. For each day that the high temperature in your hometown is at least 1 degree Fahrenheit above average, as listed by Weather Underground, you owe me $25. For each day that it is at least 1 degree Fahrenheit below average, I owe you $25.

I presume Silver is a smart guy and knows what he is doing, because in fact this is not a bet on future warming, but on past warming.  Even without a bit of future warming, he wins this bet.  Why?

I am sitting in my hotel room, and so I don’t have time to dig into the Weather Underground’s data definitions, but my guess is that their average temperatures are based on historic data, probably about a hundred years worth on average.

Over the last 100 years the world has on average warmed about 1F.  This means that today, again on average, most locations will sit on a temperature plateau about 0.5F higher than the average.  So by structuring this bet like this, he is basically asking people  to take “red” in roulette while he takes black and zero and double zero.   He has a built in 0.5F advantage.  Even with zero future warming.

Now, the whole point of this bet may be to take money from skeptics who don’t bother to educate themselves on climate and believe Rush Limbaugh or whoever that there has never been any change in world temperatures.  Fine.  I have little patience with either side of the debate that want to be vocal without educating him or herself on the basic facts.  But to say this is a bet on future warming is BS.

The other effect that may exist here (but I am less certain of the science, commenters can help me out) is that by saying “your hometown” we put the bet into the domain of urban heat islands and temperature station siting issues.  Clearly UHI has substantially increased temperatures in many cities, but that is because average temperatures are generally computed as the average of the daily minimum and maximum.  My sense is that UHI has a much bigger effect on Tmin than Tmax – such that my son and I found a 10 degree F UHI in Phoenix in the evening, but I am not sure if we could find one, or as large of one, at the daily maximum.  Nevertheless, to the extent that such an effect exists for Tmax, most cities that have grown over the last few years will be above their averages just from the increasing UHI component.

I don’t have the contents of my computer hard drive here with me, but a better bet would be from a 10-year average of some accepted metric  (I’d prefer satellites but Hadley CRUT would be OK if we just had to use the old dinosaur surface record).  Since I accept about 1-1.2C per century, I’d insist on this trend line and would pay out above it and collect below it  (all real alarmists consider a 1.2C per century future trend to be about zero probability, so I suspect this would be acceptable).